Local, instructor-led live Statistics training courses demonstrate through interactive discussion and hands-on practice how to apply Statistic principles to the solving of real-world problems. Statistics training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Canada or in NobleProg corporate training centers in Canada. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
The training was adaptable and personalized to our needs.
Dominique Soulie
Course:Minitab for Statistical Data Analysis
I liked the exercises as it's the only way to learn, by repetition.
David Rushe
Course:Tableau Advanced
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course:Data Mining & Machine Learning with R
Very tailored to needs.
Yashan Wang
Course:Data Mining with R
I genuinely enjoyed working 1:1 with Gunner.
Bryant Ives
Course:Introduction to R
A very coherent and systematic refresher on forecasting models and useful applications.
Marco D'Alterio
Course:Modelling and Forecasting for Government
I enjoyed the stats refresher and using software for exercises.
Rabeeah Shah
Course:Modelling and Forecasting for Government
I like the exercises done.
Nour Assaf
Course:Data Mining and Analysis
The hands-on exercise and the trainer capacity to explain complex topics in simple terms.
youssef chamoun
Course:Data Mining and Analysis
I learned about a lot of techniques I didn't know about before.
Alexandra Torok
Course:Modelling and Forecasting for Government
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course:Data Mining and Analysis
I mostly liked the trainer giving real live Examples.
Simon Hahn
Course:Administrator Training for Apache Hadoop
I genuinely enjoyed the big competences of Trainer.
Grzegorz Gorski
Course:Administrator Training for Apache Hadoop
I genuinely enjoyed the many hands-on sessions.
Jacek Pieczątka
Course:Administrator Training for Apache Hadoop
I liked the new insights in deep machine learning.
Josip Arneric
Course:Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course:Neural Network in R
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course:Neural Network in R
I genuinely was benefit from the flexibility of the trainer.
Irina Ostapenko
Course:Statistics Level 2
The flexible and friendly style. Learning exactly what was useful and relevant for me.
Jenny Tickner
Course:Advanced R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
Luke Pontin
Course:Data and Analytics - from the ground up
Learning how to use excel properly.
Torin Mitchell
Course:Data and Analytics - from the ground up
The way the trainer made complex subjects easy to understand.
Adam Drewry
Course:Data and Analytics - from the ground up
Detailed and comprehensive instruction given by experienced and clearly knowledgeable expert on the subject.
Justin Roche
Course:Data and Analytics - from the ground up
Tamil is very knowledgeable and nice person, I have learned from him a lot.
Aleksandra Szubert
Course:Data and Analytics - from the ground up
I liked the first session. Very intensive and quick.
Digital Jersey
Course:Data and Analytics - from the ground up
I mostly liked the patience of Tamil.
Laszlo Maros
Course:Data and Analytics - from the ground up
I really was benefit from the real life practical examples.
Wioleta (Vicky) Celinska-Drozd
Course:Data and Analytics - from the ground up
A lot of knowledge - theoretical and practical.
Anna Alechno
Course:Forecasting with R
I genuinely liked his knowledge and practical examples.
Irina Tulgara
Course:Forecasting with R
Overview and understanding how big the topic is.
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Course:Forecasting with R
Hands on examples were the most helpful.
Sean Kaukas
Course:Introduction to R
The practical exercises were extremely beneficial.
Vascutek Ltd
Course:Minitab for Statistical Data Analysis
The use of examples, though even these were demonstrated at some considerable speed.
Vascutek Ltd
Course:Minitab for Statistical Data Analysis
I enjoyed the 2nd day we did lots of examples of gauge R&R.
Vascutek Ltd
Course:Minitab for Statistical Data Analysis
I genuinely liked the exercises - use of Minicab.
Vascutek Ltd
Course:Minitab for Statistical Data Analysis
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
I really enjoyed the introduction of new packages.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
The tutor, Mr. Michael An, interacted with the audience very well, the instruction was clear. The tutor also go extent to add more information based on the requests from the students during the training.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
Good overview of R and good range of topics. Trainer was happy to answer all questions.
Symphony EYC
Course:R
I really enjoyed the knowledge of the trainer.
Stephanie Seiermann
Course:R
It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.
Sonja Steiner - BearingPoint GmbH
Course:R Programming for Data Analysis
It was very hands-on, we spent half the time actually doing things in Clouded/Hardtop, running different commands, checking the system, and so on. The extra materials (books, websites, etc. .) were really appreciated, we will have to continue to learn. The installations were quite fun, and very handy, the cluster setup from scratch was really good.
Ericsson
Course:Administrator Training for Apache Hadoop
I really liked the exercises on time series modeling.
Teleperformance
Course:Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
Michael Lopez - Teleperformance
Course:Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
Teleperformance
Course:Data Analytics With R
The training felt very personal and customised to what I wanted out of it. It was so non judgemental that I felt comfortable in asking whatever questions I needed to, and the trainer was able to competently answer all my questions. It was a very good overview of useful statistical techniques and there was a good balance between statistical theory and how to use minitab.
Cambridge Consultants
Course:Minitab for Statistical Data Analysis
Lot of hands-on exercises.
Ericsson
Course:Administrator Training for Apache Hadoop
Ambari management tool. Ability to discuss practical Hadoop experiences from other business case than telecom.
Ericsson
Course:Administrator Training for Apache Hadoop
I generally liked the trainer Knowledge.
SAP Business Objects
Course:Statistics Level 1
| Code | Name | Duration | Overview |
|---|---|---|---|
| mrkfct | Market Forecasting | 14 hours | Audience This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting. Description This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data. It uses standard tools like Microsoft Excel or some Open Source programs (notably R project). The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...) |
| predmodr | Predictive Modelling with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use R to create deep learning models for banking - Build their own deep learning credit risk model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use R to create deep learning models for finance - Build their own deep learning stock price prediction model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlfinancer | Machine Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: - Understand the fundamental concepts in machine learning - Learn the applications and uses of machine learning in finance - Develop their own algorithmic trading strategy using machine learning with R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| intermediaterforfinance | Intermediate R for Finance | 21 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples. By the end of this training, participants will be able to: - Implement advanced R programming techniques - Use R to manipulate their data to perform more advanced financial operations Audience - Programmers - Finance professionals - IT Professionals Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| rforfinance | R Programming for Finance | 28 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: - Understand the fundamentals of the R programming language - Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) - Build applications that solve problems related to asset allocation, risk analysis, investment performance and more - Troubleshoot, integrate deploy and optimize an R application Audience - Developers - Analysts - Quants Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| mlbankingr | Machine Learning for Banking (with R) | 28 hours | In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience - Developers - Data scientists - Banking professionals with a technical background Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 hours | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: - Perform data analysis and create appealing visualizations - Draw useful conclusions from various datasets of sample data - Filter, sort and summarize data to answer exploratory questions - Turn processed data into informative line plots, bar plots, histograms - Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience - Beginners to the R language - Beginners to data analysis and data visualization Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| radvml | Advanced Machine Learning with R | 21 hours | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: - Use techniques as hyper-parameter tuning and deep learning - Understand and implement unsupervised learning techniques - Put a model into production for use in a larger application Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| danagr | Data and Analytics - from the ground up | 42 hours | Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: What has happened? - processing and analyzing data - producing informative data visualizations What will happen? - forecasting future performance - evaluating forecasts What should happen? - turning data into evidence-based business decisions - optimizing processes The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs. |
| nlpwithr | NLP: Natural Language Processing with R | 21 hours | It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| advr | Advanced R | 7 hours | This course covers advanced topics in R programming. |
| intror | Introduction to R with Time Series Analysis | 21 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| dmmlr | Data Mining & Machine Learning with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| rprogda | R Programming for Data Analysis | 14 hours | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| dataar | Data Analytics With R | 21 hours | [R](http://www.r-project.org/) is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on |
| rintrob | Introductory R for Biologists | 28 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. |
| webappsr | Building Web Applications in R with Shiny | 7 hours | Description: This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS. Objective: Covers the basics of how Shiny apps work. Covers all commonly used input/output/rendering/paneling functions from the Shiny library. |
| rlang | R | 21 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. |
| MLFWR1 | Machine Learning Fundamentals with R | 14 hours | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| bigdatar | Programming with Big Data in R | 21 hours | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| dataminr | Data Mining with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| rprogadv | Advanced R Programming | 7 hours | This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills. The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience |
| rneuralnet | Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software. |
| mrkanar | Marketing Analytics using R | 21 hours | Audience Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals. Overview The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech. Format Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course. |
| shinyrhtml | Shiny, R and HTML: Merging Data Science and Web Development | 7 hours | Shiny is an open source R package that provides a web framework for building interactive web applications using R. In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML. By the end of this training, participants will be able to: - Build interactive web applications with R using Shiny Audience - Data scientists - Web developers - Statisticians Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| frcr | Forecasting with R | 14 hours | This course allows delegate to fully automate the process of forecasting with R |
| rintro | Introduction to R | 21 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software ([http://www.r-project.org](http://www.r-project.org/)) both on a command line and in a graphical user interface (GUI). |
| rdataana | R for Data Analysis and Research | 7 hours | Audience - managers - developers - scientists - students Format of the course on-line instruction and discussion OR face-to-face workshops |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Data Mining & Machine Learning with R - NS, Halifax - Hampton Inn | Mon, Nov 5 2018, 9:30 am | CA$5,555 / CA$7,555 |
| Forecasting with R - Winnipeg - 201 Portage Avenue | Mon, Nov 5 2018, 9:30 am | CA$4,730 / CA$6,550 |
| Statistical Analysis using SPSS - Oakville - Winston Park | Mon, Nov 5 2018, 9:30 am | CA$6,985 / CA$8,785 |
| Statistics for Managers - Ottawa – Albert & Metcalfe | Mon, Nov 5 2018, 9:30 am | CA$11,495 / CA$13,795 |
| Market Forecasting - Montreal - University Street | Tue, Nov 6 2018, 9:30 am | CA$4,730 / CA$6,660 |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Mobile Automation with Appium Overview | Vancouver - Park Place | Mon, Oct 22 2018, 9:30 am | N/A / CA$3,893 |
| Web Application Performance Testing with JMeter | Toronto - West Toronto - Etobicoke | Wed, Oct 31 2018, 9:30 am | CA$2,450 / CA$4,080 |
| Drupal 7 for Developers | Oakville - Winston Park | Wed, Nov 7 2018, 9:30 am | CA$4,683 / CA$6,383 |
| Typescript | Ottawa – Albert & Metcalfe | Thu, Nov 8 2018, 9:30 am | CA$2,228 / CA$3,888 |
| CISA - Certified Information Systems Auditor | Toronto - West Toronto - Etobicoke | Tue, Nov 20 2018, 9:30 am | N/A / CA$10,336 |
| Haskell Fundamentals | Ottawa – Albert & Metcalfe | Tue, Nov 27 2018, 9:30 am | CA$3,870 / CA$5,690 |
| Adobe LiveCycle Designer | Burnaby - Metrotown | Thu, Dec 6 2018, 9:30 am | CA$4,683 / CA$6,513 |
| Data Mining with R | NS, Halifax - Hampton Inn | Thu, Jan 31 2019, 9:30 am | CA$3,870 / CA$5,870 |
| OCEB Certified Expert in BPM - Business Advanced Exam Preparation | Calgary - Sun Life | Fri, Feb 15 2019, 9:30 am | CA$2,025 / CA$3,705 |
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